Why does the loss decrease and the accuracy not? PyTorch

Question

I'm creating a CNN in Pytorch and I'm having some problem with the training function, I believe.

For each epoch, the loss decreases. But the accuracy remains the same, it doesn't change. The output of the training function is this:

Epoch: 1
correct: 234, N_test: 468 ------>  loss: 58.2041027, accuracy_val: %50.0
Epoch: 2
correct: 234, N_test: 468 ------>  loss: 51.47981386, accuracy_val: %50.0
Epoch: 3
correct: 234, N_test: 468 ------>  loss: 51.57150275, accuracy_val: %50.0
Epoch: 4
correct: 234, N_test: 468 ------>  loss: 39.14232715, accuracy_val: %50.0
Epoch: 5
correct: 234, N_test: 468 ------>  loss: 32.23730827, accuracy_val: %50.0

I know that although they are correlated, loss and accuracy have their complications, but I believe there may be a problem with the code and I am not able to determine what.

Here's the neural network:

class CNN(nn.Module):

# Contructor
def __init__(self):
    super(CNN, self).__init__()
    # Conv1
    self.cnn1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=1, padding=0)
    self.conv1_bn = nn.BatchNorm2d(64)
    self.maxpool1=nn.MaxPool2d(kernel_size=2, stride=2)
    
    # Conv2
    self.cnn2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=5,stride=1, padding=0)
    self.conv2_bn = nn.BatchNorm2d(64)
    self.maxpool2=nn.MaxPool2d(kernel_size=2, stride=2)  
    
    # Conv3
    self.cnn3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5,stride=1, padding=0)
    self.conv3_bn = nn.BatchNorm2d(128)
    self.maxpool3=nn.MaxPool2d(kernel_size=2, stride=2)
    
    
    # FCL 1
    self.fc1 = nn.Linear(in_features=128 * 27 * 27, out_features=500)
    self.bn_fc1 = nn.BatchNorm1d(500)
    
    # FCL 2
    self.fc2 = nn.Linear(in_features=500, out_features=500)
    self.bn_fc2 = nn.BatchNorm1d(500)
    
    # FCL3
    self.fc3 = nn.Linear(in_features=500, out_features=1)
    

    

# Prediction
def forward(self, x):
    # conv1
    x = self.cnn1(x)
    x = self.conv1_bn(x)
    x = torch.relu(x)
    x = self.maxpool1(x)
    # conv2
    x = self.cnn2(x)
    x = self.conv2_bn(x)
    x = torch.relu(x)
    x = self.maxpool2(x)
    # conv3
    x = self.cnn3(x)
    x = self.conv3_bn(x)
    x = torch.relu(x)
    x = self.maxpool3(x)
    
    # Fcl1
    x = x.view(x.size(0), -1)
    x = self.fc1(x)
    x = self.bn_fc1(x)
    x = torch.relu(x)
    # Fcl2
    x = self.fc2(x)
    x = self.bn_fc2(x)
    x = torch.relu(x)
    # final fcl
    x = self.fc3(x)
    x = torch.sigmoid(x)
   
    return x

The training function:

def train_model(model,train_loader,test_loader,optimizer,n_epochs=5):

#global variable 
N_test=len(dataset_val)
accuracy_list=[]

loss_list=[]
for epoch in range(n_epochs):
    cost = 0
    model.train()
    print(f"Epoch: {epoch + 1}")
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        z = model(x)
        y = y.unsqueeze(-1)
        y = y.float()
        loss = criterion(z, y)
        loss.backward()
        optimizer.step()
        cost+=loss.item()

    correct=0
    model.eval()
    #perform a prediction on the validation  data  
    for x_test, y_test in test_loader:
        x_test, y_test = x_test.to(device), y_test.to(device)
        
        z = model(x_test)
        _, yhat = torch.max(z.data, 1)
        correct += (yhat == y_test).sum().item()

    accuracy = correct / N_test
    accuracy_list.append(accuracy)
    loss_list.append(cost)
    print(f"------>  loss: {round(cost, 8)}, accuracy_val: %{accuracy * 100}")

 
return accuracy_list, loss_lis

The plot is this:

Plot with accuracy and loss

Answer 1

Your outputs are all going to be 1 since you have 1 output and you're taking the max over the 2nd dimension:

    _, yhat = torch.max(z.data, 1)
    correct += (yhat == y_test).sum().item()

To do binary classification you need to pick a threshold and then threshold your data into two classes, or have 2 outputs (probably easier in this case).

Answer 2

I removed the sigmoid function from the last layer and replaced BCELoss() with CrossEntropyLoss() and worked!

Also, as @jhso said, to do binary classification a threshold is needed and we must threshold the data into two classes, or have 2 outputs (probably easier in this case).